A SALSA-Based Literature Review On Federated Learning: Taxonomy Of Challenges And Emerging Applications

Abstract

Federated Learning (FL) has emerged as a transformative approach for privacy-preserving machine learning, enabling decentralized model training across distributed data sources. This survey presents a comprehensive andmethodologicallyrigorous review of FL systems,employing the SALSA methodology to identify, evaluate, and categorize the primary challenges associated with FL implementations. Spanning literature from 2015 to 2025, the review covers both tradi- tional sectors such as healthcare, finance, IoT, and education, as well as underrepresented and emerging domains including smart agriculture, wildlife conservation, legal
analytics, and space exploration. We introduce a structured taxonomy that classifies FL challenges into six key cate- gories: privacy and security, communication and infrastructure, data heterogeneity, algorithmic and optimization, fairness and participation, and evaluation and debugging. The qualitative findings reveal critical gaps in current research, especially regarding cross-domain applicabil- ity, fairness, client reliability, and scalable personalization. Additionally, the survey identifies significant under representation of FL in agriculture and low resource environments, proposing application specific adaptations to enhance
deployment feasibility. Emerging opportunities are discussed in the context of intelligent edge systems, collaborative governance, and regulatory compliance. Comparative tables and domain specific summaries further enhance the practical value of this work. This review contributes actionable insights for researchers, developers, and policymakers seeking to design robust, inclusive, and secure FL frameworks. It establishes a foun- dation for future innovation and emphasizes the need for scalable, trustworthy federated systems across privacy-sensitive domains in the era of distributed artificial intelligence.